The adult mammalian lumbar spinal cord can learn to step in the absence of descending input from the brain. The ability of the spinal cord to learn is an extremely important finding for tens of thousands of spinal cord injured patients, as it could mean the difference between being confined to a wheelchair or being able to stand and take some steps. Understanding how to teach the spinal cord to step through effective rehabilitative training has immediate clinical application in itself and can also play a crucial role in enhancing the efficacy of other potential therapeutic interventions for spinal cord injuries. One method of rehabilitative training, i.e. body weight supported locomotion on a treadmill, has been successful in enhancing locomotor recovery in spinal cord injured animals. There is growing evidence that this form of training can also be used to improve walking in humans that have suffered a stroke or spinal cord injury. The success of training, however, depends on the generation of appropriate patterns of sensory information during weight bearing stepping. We hypothesize that repetitive stimulation of key load and phase-related afferent signals during step training reinforces the circuits in the lumbar spinal cord that generate locomotion. We propose to use a robotic system to enhance the locomotor training of spinally transected rats to step on a treadmill. The robotic system provides precise control of weight bearing and of the forces applied to the hindlimbs during stepping. The robotic system will be used to test the effectiveness of maximizing load-related sensory information during stepping by continually adapting weight bearing and by enhancing weight bearing beyond what is possible with current weight support techniques. The robotic system will also be used to determine the effects of imposing patterns of hindlimb coordination on the recovery of stable and consistent stepping and will also determine the extent that mechanical assistance during training should be provided in order to facilitate the generation of stepping. The findings will provide a needed behavioral foundation for future research that identifies the specific neurophysiological and molecular mechanisms underlying sensory-enhanced spinal learning. These data will also provide insight into development of body weight support control and manual (or robotic) intervention for human locomotor training.